Detection of avian influenza-infected chickens based on a chicken sound convolutional neural network

Abstract

The modern poultry industry is large-scale and breeding-intensive, making the spread of disease in poultry easier, faster and more harmful. Avian influenza (AI) is the most important disease in poultry, and prevention and detection of avian influenza in poultry is a focus of scientific research and the poultry industry. In this paper, a new sound recognition method, the chicken sound convolutional neural network (CSCNN), is proposed for detection of chickens with avian influenza. According to the spectral differences in environmental noise, chicken behaviour noise and chicken sound, a method was designed to extract the chicken sound from complex sound data. Four features of the chicken sounds were calculated and combined into feature maps, including Logfbank, Mel Frequency Cepstrum Coefficient (MFCC), MFCC Delta and MFCC Delta-Delta. Finally, the sounds of healthy chickens and chickens with avian influenza were recognized using CSCNN. In the experiment, the recognition accuracies of CSCNN via spectrogram (CSCNN-S) were 93.01%, 95.05%, and 97.43% on the 2nd, 4th, and 6th day after injection with the H9N2 virus, and the recognition accuracies of CSCNN with feature mapping (CSCNN-F) were 89.79%, 93.56%, and 95.84%, respectively. The experimental results show that the method proposed in this paper can be used to quickly and effectively detect avian influenza-infected chickens via chicken sound.